Elasticsearch 与 Kafka 整合剖析

Java框架

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2019-5-5

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1.概述

  目前,随着大数据的浪潮,Kafka 被越来越多的企业所认可,如今的Kafka已发展到0.10.x,其优秀的特性也带给我们解决实际业务的方案。对于数据分流来说,既可以分流到离线存储平台(HDFS),离线计算平台(Hive仓库),也可以分流实时流水计算(Storm,Spark)等,同样也可以分流到海量数据查询(HBase),或是及时查询(ElasticSearch)。而今天笔者给大家分享的就是Kafka 分流数据到 ElasticSearch。

2.内容

  我们知道,ElasticSearch是有其自己的套件的,简称ELK,即ElasticSearch,Logstash以及Kibana。ElasticSearch负责存储,Logstash负责收集数据来源,Kibana负责可视化数据,分工明确。想要分流Kafka中的消息数据,可以使用Logstash的插件直接消费,但是需要我们编写复杂的过滤条件,和特殊的映射处理,比如系统保留的`_uid`字段等需要我们额外的转化。今天我们使用另外一种方式来处理数据,使用Kafka的消费API和ES的存储API来处理分流数据。通过编写Kafka消费者,消费对应的业务数据,将消费的数据通过ES存储API,通过创建对应的索引的,存储到ES中。其流程如下图所示:

  上图可知,消费收集的数据,通过ES提供的存储接口进行存储。存储的数据,这里我们可以规划,做定时调度。最后,我们可以通过Kibana来可视化ES中的数据,对外提供业务调用接口,进行数据共享。

3.实现

  下面,我们开始进行实现细节处理,这里给大家提供实现的核心代码部分,实现代码如下所示:

3.1 定义ES格式

  我们以插件的形式进行消费,从Kafka到ES的数据流向,只需要定义插件格式,如下所示:

{
    "job": {
        "content": {
            "reader": {
                "name": "kafka",
                "parameter": {
                    "topic": "kafka_es_client_error",
                    "groupid": "es2",
                    "bootstrapServers": "k1:9094,k2:9094,k3:9094"
                },
                "threads": 6
            },
            "writer": {
                "name": "es",
                "parameter": {
                    "host": [
                        "es1:9300,es2:9300,es3:9300"
                    ],
                    "index": "client_error_%s",
                    "type": "client_error"
                }
            }
        }
    }
}

  这里处理消费存储的方式,将读和写的源分开,配置各自属性即可。

3.2 数据存储

  这里,我们通过每天建立索引进行存储,便于业务查询,实现细节如下所示:

public class EsProducer {

    private final static Logger LOG = LoggerFactory.getLogger(EsProducer.class);
    private final KafkaConsumer<String, String> consumer;
    private ExecutorService executorService;
    private Configuration conf = null;
    private static int counter = 0;

    public EsProducer() {
        String root = System.getProperty("user.dir") + "/conf/";
        String path = SystemConfigUtils.getProperty("kafka.x.plugins.exec.path");
        conf = Configuration.from(new File(root + path));
        Properties props = new Properties();
        props.put("bootstrap.servers", conf.getString("job.content.reader.parameter.bootstrapServers"));
        props.put("group.id", conf.getString("job.content.reader.parameter.groupid"));
        props.put("enable.auto.commit", "true");
        props.put("auto.commit.interval.ms", "1000");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        consumer = new KafkaConsumer<String, String>(props);
        consumer.subscribe(Arrays.asList(conf.getString("job.content.reader.parameter.topic")));
    }

    public void execute() {
        executorService = Executors.newFixedThreadPool(conf.getInt("job.content.reader.threads"));
        while (true) {
            ConsumerRecords<String, String> records = consumer.poll(100);
            if (null != records) {
                executorService.submit(new KafkaConsumerThread(records, consumer));
            }
        }
    }

    public void shutdown() {
        try {
            if (consumer != null) {
                consumer.close();
            }
            if (executorService != null) {
                executorService.shutdown();
            }
            if (!executorService.awaitTermination(10, TimeUnit.SECONDS)) {
                LOG.error("Shutdown kafka consumer thread timeout.");
            }
        } catch (InterruptedException ignored) {
            Thread.currentThread().interrupt();
        }
    }

    class KafkaConsumerThread implements Runnable {

        private ConsumerRecords<String, String> records;

        public KafkaConsumerThread(ConsumerRecords<String, String> records, KafkaConsumer<String, String> consumer) {
            this.records = records;
        }

        @Override
        public void run() {
            String index = conf.getString("job.content.writer.parameter.index");
            String type = conf.getString("job.content.writer.parameter.type");
            for (TopicPartition partition : records.partitions()) {
                List<ConsumerRecord<String, String>> partitionRecords = records.records(partition);
                for (ConsumerRecord<String, String> record : partitionRecords) {
                    JSONObject json = JSON.parseObject(record.value());
                    List<Map<String, Object>> list = new ArrayList<>();
                    Map<String, Object> map = new HashMap<>();
                    index = String.format(index, CalendarUtils.timeSpan2EsDay(json.getLongValue("_tm") * 1000L));
                    
                    if (counter < 10) {
                        LOG.info("Index : " + index);
                        counter++;
                    }
                    
                    for (String key : json.keySet()) {
                        if ("_uid".equals(key)) {
                            map.put("uid", json.get(key));
                        } else {
                            map.put(key, json.get(key));
                        }
                        list.add(map);
                    }
                    
                    EsUtils.write2Es(index, type, list);
                }
            }
        }

    }

}

  这里消费的数据源就处理好了,接下来,开始ES的存储,实现代码如下所示:

public class EsUtils {

	private static TransportClient client = null;

	static {
		if (client == null) {
			client = new PreBuiltTransportClient(Settings.EMPTY);
		}
		String root = System.getProperty("user.dir") + "/conf/";
		String path = SystemConfigUtils.getProperty("kafka.x.plugins.exec.path");
		Configuration conf = Configuration.from(new File(root + path));
		List<Object> hosts = conf.getList("job.content.writer.parameter.host");
		for (Object object : hosts) {
			try {
				client.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName(object.toString().split(":")[0]), Integer.parseInt(object.toString().split(":")[1])));
			} catch (Exception e) {
				e.printStackTrace();
			}
		}
	}

	public static void write2Es(String index, String type, List<Map<String, Object>> dataSets) {

		BulkRequestBuilder bulkRequest = client.prepareBulk();
		for (Map<String, Object> dataSet : dataSets) {
			bulkRequest.add(client.prepareIndex(index, type).setSource(dataSet));
		}

		bulkRequest.execute().actionGet();
		// if (client != null) {
		// client.close();
		// }
	}

	public static void close() {
		if (client != null) {
			client.close();
		}
	}	
}

  这里,我们利用BulkRequestBuilder进行批量写入,减少频繁写入率。

4.调度

  存储在ES中的数据,如果不需要长期存储,比如:我们只需要存储及时查询数据一个月,对于一个月以前的数据需要清除掉。这里,我们可以编写脚本直接使用Crontab来进行简单调用即可,脚本如下所示:

#!/bin/sh
# <Usage>: ./delete_es_by_day.sh kafka_error_client logsdate 30 </Usage>
echo "<Usage>: ./delete_es_by_day.sh kafka_error_client logsdate 30 </Usage>"


index_name=$1
daycolumn=$2
savedays=$3
format_day=$4

if [ ! -n "$savedays" ]; then
  echo "Oops. The args is not right,please input again...."
  exit 1
fi

if [ ! -n "$format_day" ]; then
   format_day='%Y%m%d'
fi

sevendayago=`date -d "-${savedays} day " +${format_day}`

curl -XDELETE "es1:9200/${index_name}/_query?pretty" -d "
{
        "query": {
                "filtered": {
                        "filter": {
                                "bool": {
                                        "must": {
                                                "range": {
                                                        "${daycolumn}": {
                                                                "from": null,
                                                                "to": ${sevendayago},
                                                                "include_lower": true,
                                                                "include_upper": true
                                                        }
                                                }
                                        }
                                }
                        }
                }
        }
}"

echo "Finished."

然后,在Crontab中进行定时调度即可。

5.总结

  这里,我们在进行数据写入ES的时候,需要注意,有些字段是ES保留字段,比如`_uid`,这里我们需要转化,不然写到ES的时候,会引发冲突导致异常,最终写入失败。

6.结束语

  这篇博客就和大家分享到这里,如果大家在研究学习的过程当中有什么问题,可以加群进行讨论或发送邮件给我,我会尽我所能为您解答,与君共勉

作者:哥不是小萝莉